In previous posts we have seen – and critiqued – ideas about the causes of ice age inception and ice age termination being due to high latitude insolation. These ideas are known under the banner of “Milankovitch forcing”. Mostly I’ve presented the concept by plotting insolation data at particular latitudes, in one form or another. The insolation at different latitudes depends on obliquity and precession (as well as eccentricity).
Obliquity is the tilt of the earth’s axis – which varies over about 40,000 year cycles. Precession is the movement of point of closest approach (perihelion) and how it coincides with northern hemisphere summer – this varies over about a 20,000 year cycle. The effect of precession is modified by the eccentricity of the earth’s axis – which varies over a 100,000 year cycle.
If the earth’s orbit was a perfect circle (eccentricity = 0) then “precession” would have no effect, because the earth would be a constant distance from the sun. As eccentricity increases the impact of precession gets bigger.
How to understand these ideas better?
Peter Huybers has a nice explanation and presentation of obliquity and precession in his 2007 paper, along with some very interesting ideas that we will follow up in a later article.
The top graph shows the average insolation value by latitude and day of the year (over 2M years). The second graph shows the anomaly compared with the average at times of maximum obliquity. The third graph shows the anomaly compared with the average at times of maximum precession. The graphs to the right show the annual average of these values:
We can see immediately that times of maximum precession (bottom graph) have very little impact on annual averages (the right side graph). This is because the increase in, say, the summer/autumn, are cancelled out by the corresponding decreases in the spring.
But we can also see that times of maximum obliquity (middle graph) DO impact on annual averages (right side graph). Total energy is shifted to the poles from the tropics .
I was trying, not very effectively, to explain some of this in (too many graphs) in Part Five – Obliquity & Precession Changes.
Here is another way to look at this concept. For the last 500 kyrs, I plotted out obliquity and precession modified by eccentricity (e sin w) in the top graph, and in the bottom graph the annual anomaly by latitude and through time. WordPress kind of forces everything into 500 pixel wide graphs which doesn’t help too much. So click on it to get the HD version:
Figure 2 – Click to Expand
It is easy to see that the 40,000 year obliquity cycles correspond to high latitude (north & south) anomalies, which last for considerable periods. When obliquity is high, the northern and southern high latitude regions have an increase in annual average insolation. When obliquity is low, there is a decrease. If we look at the precession we don’t see a corresponding change in the annual average (because one season’s increase mostly cancels out the other season’s decrease).
Huybers’ paper has a lot more to it than that, and I recommend reading it. He has a 2M yr global proxy database, that isn’t dependent on “orbital tuning” (note 1) and an interesting explanation and demonstration for obliquity as the dominant factor in “pacing” the ice ages. We will come back to his ideas.
In the meantime, I’ve been collecting various data sources. One big challenge in understanding ice ages is that the graphs in the various papers don’t allow you to zoom in on the period of interest. I thought I could help to fix that by providing the data - and comparing the data – in High Definition instead of snapshots of 800,000 years on half the width of a standard pdf. It’s a work in progress..
The top graph (below) has two versions of temperature proxy. One is Huyber’s global proxy for ice volume (δ18O) from deep ocean cores, while the other is the local proxy for temperature (δD) from Dome C core from Antarctica (75°S). This location is generally known as EDC, i.e., EPICA Dome C. The two datasets are laid out on their own timescales (more on timescales below):
Figure 3 – Click to Expand
The middle graph has CO2 and CH4 from Dome C. It’s amazing how tightly CO2 and CH4 are linked to the temperature proxies and to each other. (The CO2 data comes from Lüthi et al 2008, and the CH4 data from Loulergue et al 2008).
The bottom graph has obliquity and annual insolation anomaly area-averaged over 70ºS-90ºS. Because we are looking at annual insolation anomaly this value is completely in phase with obliquity.
Why are the two datasets on the top graph out of alignment? I don’t know the full answer to this yet. Obviously the lag from the atmosphere to the deep ocean is part of the explanation.
Here is a 500 kyr comparison of LR04 (Lisiecki & Raymo 2005) and Huybers’ dataset – both deep ocean cores – but LR04 uses ‘orbital tuning’. The second graph has obliquity & precession (modified by eccentricity). The third graph has EDC from Antarctica:
Figure 4 – Click to Expand
Now we zoom in on the last 150 kyrs with two Antarctic cores on the top graph and NGRIP (North Greenland) on the bottom graph:
Figure 5 – Click to Expand
Here we see EDML (high resolution Antarctic core) compared with NGRIP (Greenland) over the last 150 kyrs (NGRIP only goes back to 123 kyrs) plus CO2 & CH4 from EDC – once again, the tight correspondence of CO2 and CH4 with the temperature records from both polar regions is amazing:
Figure 6 – Click to Expand
The comparison and linking of “abrupt climate change” in Greenland and Antarctic has been covered in EPICA 2006 (note the timescale is in the opposite direction to the graphs above):
Figure 7 – Click to Expand
As most papers acknowledge, providing data on the most accurate “assumption free” timescales is the Holy Grail of ice age analysis. However, there are no assumption-free timescales. But lots of progress has been made.
Huybers’ timescale is based primarily on a) a sedimentation model, b) tying together the various identified inception & termination points for each of the proxies, c) the independently dated Brunhes- Matuyama reversal at 780,000 years ago.
The EDC (EPICA Dome ‘C’) timescale is based on a variety of age markers:
- for the first 50 kyrs by tying the data to Greenland (via high resolution CH4 in both records) which can be layer counted because of much higher precipitation
- volcanic eruptions
- 10Be events which can be independently dated
- ice flow models – how ice flows and compresses under pressure
- finally, “orbital tuning”
EDC2 was the timescale on which the data was presented in the seminal 2004 EPICA paper. This 2004 paper presented the EDC core going back over 800 kyrs (previously the Vostok core was the longest, going back 400 kyrs). The EPICA 2006 paper was the Dronning Maud Land Core (EDML) which covered a shorter time (150 kyrs) but at higher resolution, allowing a better matchup between Antarctica and Greenland. This introduced the improved EDC3 timescale.
In a technical paper on dating, Parannin et al 2007 show the differences between EDC3 and EDC2 and also between EDC3 and LR04.
Figure 8 – Click to Expand
So if you have data, you need to understand the timescale it is plotted on.
I have the EDC3 timescale in terms of depth so next I’ll convert the EDC temperature proxy (δD) on EDC2 to EDC3 time. I also have dust vs depth for the EDC core – another fascinating variable that is about 25 times stronger during peak glacials compared with interglacials – this needs converting to the EDC3 timescale. Other data includes some other atmospheric chemical components. Then I have NGRIP data (North Greenland) going back 123,000 years but on the original 2004 timescale, and it has been relaid onto GICC05 timescale (still to find).
Very recently (mid 2013) a new Antarctic timescale was proposed – AICC2012 – which brings all of the Antarctic ice cores onto one common timescale. See references below.
Calling Matlab gurus – plotting many items onto one graph has some benefits. Matlab is an excellent tool but I haven’t yet figured out how to plot lots of data onto the same graph. If multiple data sources have the same x-series data and a similar y-range there is no problem. If I have two data sources with similar x values (but different x-series data) and completely different y values I can use plotyy. How about if I have five datasources, each with different but similar x-series and different y-values. How do I plot them on one graph, and display the multiple y-axes (easily)?
This article was intended to highlight obliquity and precession in a different and hopefully more useful way. And to begin to present some data in high resolution.
Articles in the Series
Part One – An introduction
Part Two – Lorenz - one point of view from the exceptional E.N. Lorenz
Part Three – Hays, Imbrie & Shackleton - how everyone got onto the Milankovitch theory
Part Four – Understanding Orbits, Seasons and Stuff - how the wobbles and movements of the earth’s orbit affect incoming solar radiation
Part Five – Obliquity & Precession Changes - and in a bit more detail
Part Six – “Hypotheses Abound” - lots of different theories that confusingly go by the same name
Part Seven – GCM I - early work with climate models to try and get “perennial snow cover” at high latitudes to start an ice age around 116,000 years ago
Part Seven and a Half – Mindmap - my mind map at that time, with many of the papers I have been reviewing and categorizing plus key extracts from those papers
Part Eight – GCM II - more recent work from the “noughties” – GCM results plus EMIC (earth models of intermediate complexity) again trying to produce perennial snow cover
Part Nine – GCM III - very recent work from 2012, a full GCM, with reduced spatial resolution and speeding up external forcings by a factors of 10, modeling the last 120 kyrs
Part Ten – GCM IV - very recent work from 2012, a high resolution GCM called CCSM4, producing glacial inception at 115 kyrs
Pop Quiz: End of An Ice Age - a chance for people to test their ideas about whether solar insolation is the factor that ended the last ice age
Eleven – End of the Last Ice age - latest data showing relationship between Southern Hemisphere temperatures, global temperatures and CO2
Twelve – GCM V – Ice Age Termination - very recent work from He et al 2013, using a high resolution GCM (CCSM3) to analyze the end of the last ice age and the complex link between Antarctic and Greenland
Thirteen – Terminator II - looking at the date of Termination II, the end of the penultimate ice age – and implications for the cause of Termination II
Glacial variability over the last two million years: an extended depth-derived agemodel, continuous obliquity pacing, and the Pleistocene progression, Peter Huybers, Quaternary Science Reviews (2007) – free paper
Eight glacial cycles from an Antarctic ice core, EPICA community members, Nature (2004) – free paper
One-to-one coupling of glacial climate variability in Greenland and Antarctica, EPICA Community Members, Nature (2006) – free paper
High-resolution carbon dioxide concentration record 650,000–800,000 years before present, Lüthi et al, Nature (2008)
Orbital and millennial-scale features of atmospheric CH4 over the past 800,000 years, Loulergue et al, Nature (2008)
A Pliocene-Pleistocene stack of 57 globally distributed benthic D18O records, Lorraine Lisiecki & Maureen E. Raymo, Paleoceanography (2005) – free paper
The EDC3 chronology for the EPICA Dome C ice core, Parennin et al, Climate of the Past (2007) – free paper
An optimized multi-proxy, multi-site Antarctic ice and gas orbital chronology (AICC2012): 120–800 ka, L. Bazin et al, Climate of the Past (2013) – free paper
The Antarctic ice core chronology (AICC2012): an optimized multi-parameter and multi-site dating approach for the last 120 thousand years, D. Veres et al, Climate of the Past (2013) – free paper
Note 1 – See for example Thirteen – Terminator II, under the heading What is the basis for the SPECMAP dating?
It is important to understand the assumptions built into every ice age database.
Huybers 2007 continues the work of HW04 (Huybers & Wunsch 2004) which attempts to produce a global proxy datbase (a proxy for global ice volume) without any assumptions relating to the “Milankovitch theory”.